Quantitative model of near infrared spectroscopy based on pretreatment combined with parallel convolution neural network

[1]  Pixiang Wang,et al.  Comparison of NIR and Raman spectrometries as quantitative methods to monitor polyethylene content in recycled polypropylene , 2023, Polymer Testing.

[2]  Chengmao Cao,et al.  Detection Method for Walnut Shell-Kernel Separation Accuracy Based on Near-Infrared Spectroscopy , 2022, Sensors.

[3]  Zaineb M. Alhakeem,et al.  Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques , 2022, Materials.

[4]  K. Low,et al.  Multivariate calibration strategy in simultaneous determination of temperature properties of petroleum diesel by near infrared spectrometry , 2022, Journal of Near Infrared Spectroscopy.

[5]  Soumitra Banerjee,et al.  Determination of curcumin content in sunflower oil by fourier transform near infrared spectroscopy , 2022, Journal of Food Measurement and Characterization.

[6]  Yiping Du,et al.  Piecewise preprocessing of near-infrared spectra for improving prediction ability of a PLS model , 2022, Infrared Physics & Technology.

[7]  Xiaoran Fu,et al.  A TFA-CNN method for quantitative analysis in infrared spectroscopy , 2022, Infrared Physics & Technology.

[8]  Kecheng Zhang,et al.  Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation. , 2022, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[9]  R. Teófilo,et al.  Dehydration as a Tool to improve predictability of sugarcane juice carbohydrates using near-infrared spectroscopy based PLS models , 2021, Chemometrics and Intelligent Laboratory Systems.

[10]  Ke-wei Huan,et al.  Variable selection in near-infrared spectra: Application to quantitative non-destructive determination of protein content in wheat , 2021, Infrared Physics & Technology.

[11]  Xing He,et al.  Study on the identification of resistance of rice blast based on near infrared spectroscopy. , 2021, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[12]  Krishna Chaitanya Patchava,et al.  Sammon's mapping regression for the quantitative analysis of glucose from both mid infrared and near infrared spectra. , 2021, Analytical methods : advancing methods and applications.

[13]  Xinhua Zhu,et al.  Effect of spectral pretreatment on qualitative identification of adulterated bovine colostrum by near-infrared spectroscopy , 2021 .

[14]  Yi Chen,et al.  Fast quantification of total volatile basic nitrogen (TVB-N) content in beef and pork by near-infrared spectroscopy: Comparison of SVR and PLS model. , 2021, Meat science.

[15]  C. Simon,et al.  Rapid prediction of chemical composition and degree of starch cook of multi-species aquafeeds by near infrared spectroscopy , 2021 .

[16]  Guodong Deng,et al.  An approach for simultaneous monitoring the content of insensitive agent in the double-base oblate spherical propellant by application of near-infrared spectroscope and partial least squares. , 2021, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[17]  Qinqin Chai,et al.  Improved 1D convolutional neural network adapted to near-infrared spectroscopy for rapid discrimination of Anoectochilus roxburghii and its counterfeits. , 2021, Journal of pharmaceutical and biomedical analysis.

[18]  Puneet Mishra,et al.  A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit , 2021, Chemometrics and Intelligent Laboratory Systems.

[19]  P. Zhang,et al.  Investigation on spectral standardization among multi-channel of an on-line near-infrared spectrometer , 2021 .

[20]  Brahim Lakssir,et al.  Comparing CalReg performance with other multivariate methods for estimating selected soil properties from Moroccan agricultural regions using NIR spectroscopy , 2021 .

[21]  Weifeng Zhu,et al.  Application of Near-Infrared Spectroscopy Analysis Technology to Total Nucleosides Quality Control in the Fermented Cordyceps Powder Production Process , 2020, Journal of analytical methods in chemistry.

[22]  Shao-Yan Zheng,et al.  Near infrared spectroscopy combined with chemometrics to detect and quantify adulteration of maca powder , 2020 .

[23]  C. Elliott,et al.  A rapid food chain approach for authenticity screening: The development, validation and transferability of a chemometric model using two handheld near infrared spectroscopy (NIRS) devices , 2020, Talanta.

[24]  M. L. Khodra,et al.  Predicting Macronutrient of Baby Food using Near-infrared Spectroscopy and Deep Learning Approach , 2020, IOP Conference Series: Materials Science and Engineering.

[25]  Hossein Shafizadeh-Moghadam,et al.  Multiple-depth modeling of soil organic carbon using visible–near infrared spectroscopy , 2020, Geocarto International.

[26]  D. Bhatnagar,et al.  Detection of aflatoxin B1 on corn kernel surfaces using visible-near infrared spectroscopy , 2020 .

[27]  Jia Sun,et al.  Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms , 2020, Applied Sciences.

[28]  Fei Zhang,et al.  A selective ensemble preprocessing strategy for near-infrared spectral quantitative analysis of complex samples , 2020 .

[29]  Mire Zloh,et al.  Use of near infrared spectroscopy and spectral databases to assess the quality of pharmaceutical products and aid characterization of unknown components , 2019, Journal of Near Infrared Spectroscopy.

[30]  Yibin Ying,et al.  DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis. , 2019, Analytica chimica acta.

[31]  Francis L Martin,et al.  Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach , 2019, Bioinform..

[32]  Sunita Mishra,et al.  Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics , 2017 .

[33]  L. Li,et al.  Research on Indoor Thermal Environment of Rammed Earth Dwellings in Happy Village , 2014 .

[34]  Dong-Sheng Cao,et al.  An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration. , 2013, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[35]  Natalie C. Li,et al.  Rapid determination of four tobacco specific nitrosamines in burley tobacco by near-infrared spectroscopy , 2012 .

[36]  Jiewen Zhao,et al.  Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy , 2011 .

[37]  N. Xin,et al.  Near Infrared Spectral Similarity Combined with Variable Selection Method in the Quality Control of Flos Lonicerae: A Preliminary Study , 2011 .

[38]  Di Wu,et al.  Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver , 2011 .

[39]  Xueguang Shao,et al.  Multivariate calibration methods in near infrared spectroscopic analysis , 2010 .

[40]  S. Engelsen,et al.  Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy , 2000 .

[41]  Desire L. Massart,et al.  Artificial neural networks in classification of NIR spectral data: Design of the training set , 1996 .

[42]  Jianbing Chen,et al.  Prediction of Anthocyanin Content in Three Types of Blueberry Pomace by Near-Infrared Spectroscopy , 2020 .